---
title: "Design Contest"
output: 
  flexdashboard::flex_dashboard:
    source_code: embed
    theme: spacelab
    social: [ "twitter", "facebook", "menu" ]
---
```{r include=FALSE}
library(flexdashboard)
library(ggplot2)
library(tidyverse)
library(tidyr)
library(lubridate)
```

```{r load-data}
ipf_lifts <- read_csv("data/ipf_lifts.csv")
```
```{r clean_data-01}
ipf_lifts1 <- ipf_lifts %>% 
  mutate(year = lubridate::year(date))

ipf_lifts_reshape <- ipf_lifts1 %>% 
  tidyr::pivot_longer(cols = c("best3squat_kg", "best3bench_kg", "best3deadlift_kg"), names_to = "lift") %>% 
  select(name, sex, year, lift, value)
```

```{r clean_data-02}
ipf_lifts_maxes <- ipf_lifts_reshape %>% 
  group_by(year, sex, lift) %>% 
  top_n(1, value) %>% 
  ungroup %>% 
  distinct(year, lift, value, .keep_all = TRUE)
```

```{r clean_data-03}
max_pivot <- ipf_lifts_maxes %>% 
  spread(sex, value)
```

```{r clean_data-04}
male_lifts <- max_pivot %>% 
  select(-name) %>% 
  filter(!is.na(M)) %>% 
  group_by(year, lift) %>% 
  summarise(male = mean(M))

female_lifts <- max_pivot %>% 
  select(-name) %>% 
  filter(!is.na(`F`)) %>% 
  group_by(year, lift) %>% 
  summarise(female = mean(`F`))

max_lifts <- merge(male_lifts, female_lifts)

max_lifts_final <- max_lifts %>% 
  group_by(year, lift) %>% 
  mutate(diff = male - female)
```
Introduction {.storyboard}
=========================================

###  #tidytuesday-POWERLIFTING

```{r intro-01,fig.height=5, fig.width=8}
library("png")

pp <- readPNG("img/Introduction.PNG")
plot.new() 
rasterImage(pp,0,0,1,1)
```

***




#TidyTuesday - Powerlifting

Powerlifting is a strength sport and weight training exercise that consists of three attempts at maximal weight on the following three lifts: 

1.Squat

2.Bench press

3.Deadlift.



Powerlifting is becoming a popular sport now-a-days. The idea behind powerlifting is not focused on gaining muscle but rather building strength to lift as much weight as possible.


### DATA DESCRIPTION


```{r basic-distribution, fig.height=6, fig.width=12, echo=FALSE}

p_distribution <- ipf_lifts %>% 

  select(starts_with("best3")) %>% 

  rename(Squat = "best3squat_kg", 

         Bench = "best3bench_kg",

         Deadlift = "best3deadlift_kg") %>% 

  pivot_longer(cols = everything(), names_to = "type", values_to = "weight") %>% 

  filter(!is.na(weight) & weight > 0) %>% 

  ggplot(aes(x = weight)) +

    geom_histogram(binwidth = 10) +

    facet_wrap(facets = ~ type) +

  labs(title = "Distribution of maximum weights lifted",

       subtitle = "",

       x = "Maximum weight lifted (kgs)",

       y = "Count") +

    theme(axis.ticks = element_blank())



p_distribution
```

***

#About the Data

We are exploring Powerlifting Dataset focusing on international powerlifting compitations. This dataset was published in #TidyTuesday - R4DS Online Learning Community on 10-8-2019.

https://www.tidytuesday.com/

https://github.com/rfordatascience/tidytuesday

The source data is available at : https://openpowerlifting.org/data

The dataset contains 41,152 observations and 16 variables.Only IPF federation records are analyzed.

Each lifter’s efforts in different events are represented by a single row.


#MissingData

We choose some key variables and plot a simple summary of missingness. 

```{r missing-data, echo=FALSE}

library(naniar) 
p_missing_data <- ipf_lifts %>% 

  select(sex, age, bodyweight_kg, starts_with("best3"), date) %>% 

  naniar::miss_var_summary()%>%
  arrange(variable)


p_missing_data 

```



Replication {.storyboard}
=========================================

### Replicating Connor Rothschild's #tidyTuesday submission for Powerlifting

```{r viz-01}
#install.packages("devtools")
#devtools::install_github("clauswilke/ggtext")
#devtools::install_github("connorrothschild/tpltheme")
library(tpltheme)
#install.packages("ggalt")
library(ggtext)
max_lifts_final %>% 
  filter(year == 2019) %>% 
  ggplot() + 
  ggalt::geom_dumbbell(aes(y = lift,
                    x = female, xend = male),
                colour = "grey", size = 5,
                colour_x = "#D6604C", colour_xend = "#395B74") +
  labs(y = element_blank(),
       x = "Top Lift Recorded (kg)",
       title =  "How Women and Men Differ in Top Lifts",
       subtitle = "In 2019") +
  theme(plot.title = element_markdown(lineheight = 1.1, size = 20),
        plot.subtitle = element_text(size = 15)) +
  scale_y_discrete(labels = c("Bench", "Deadlift", "Squat")) +
  drop_axis(axis = "y") +
  geom_text(aes(x = female, y = lift, label = paste(female, "kg")),
            color = "#D6604C", size = 4, vjust = -2) +
  geom_text(aes(x = male, y = lift, label = paste(male, "kg")),
            color = "#395B74", size = 4, vjust = -2) +
  geom_rect(aes(xmin=430, xmax=470, ymin=-Inf, ymax=Inf), fill="grey80") +
  geom_text(aes(label=diff, y=lift, x=450), fontface="bold", size=4) +
  geom_text(aes(x=450, y=3, label="Difference"),
                     color="grey20", size=4, vjust=-3, fontface="bold")
```

***

This is the animated dumbbell plots of the difference between the best male & female powerlifters at IPF events, Using #gganimate (and faceting w/ {magick}).

github link: https://connorrothschild.github.io/tidytuesday/2019-10-08/index


This static visualization shows the heaviest lifts from each year for both men and women categories.


- The dumbell plot is matching with the theme of the dataset very well

- It's also a new kind of plot that we have not seen before

- But it is lacking some sense of time and user interactiveness


### Animating the Top lifts visualization

```{r viz-animation-01}
#install.packages('gganimate')
#install.packages("gifski")
library(gganimate)
library(gifski)
animation <- max_lifts_final %>% 
  ggplot() + 
  ggalt::geom_dumbbell(aes(y = lift,
                    x = female, xend = male),
                colour = "grey", size = 5,
                colour_x = "#D6604C", colour_xend = "#395B74") +
  labs(y = element_blank(),
       x = "Top Lift Recorded (kg)",
       title =  "How Women and Men Differ in Top Lifts",
       subtitle='\nThis plot depicts the difference between the heaviest lifts for each sex at International Powerlifting Federation\nevents over time. \n \n{closest_state}') +
  theme(plot.title = element_markdown(lineheight = 1.1, size = 25, margin=margin(0,0,0,0)),
        plot.subtitle = element_text(size = 15, margin=margin(8,0,-30,0))) +
  scale_y_discrete(labels = c("Bench", "Deadlift", "Squat")) +
  drop_axis(axis = "y") +
  geom_text(aes(x = female, y = lift, label = paste(female, "kg")),
            color = "#D6604C", size = 4, vjust = -2) +
  geom_text(aes(x = male, y = lift, label = paste(male, "kg")),
            color = "#395B74", size = 4, vjust = -2) +
  transition_states(year, transition_length = 4, state_length = 1) +
  ease_aes('cubic-in-out')

a_gif <- animate(animation, 
                 fps = 10, 
                 duration = 25,
        width = 800, height = 400, 
        renderer = gifski_renderer("./heavy_lifts_each_sex.gif"))
a_gif
```

***

- This plot added the time dimension to the plot by displaying the visual around in a time series

- This is definitely a great improvement from the previous one

- But this graph do not append the yearly data and hence the user might immediately forget the previous year's values

- Hence the user will not able to compare the change over the years and will not be able to see the trend

### A line chart of differences over time

```{r}
animation2 <- max_lifts_final %>% 
  ungroup %>% 
  mutate(lift = case_when(lift == "best3bench_kg" ~ "Bench",
                          lift == "best3squat_kg" ~ "Squat",
                          lift == "best3deadlift_kg" ~ "Deadlift")) %>% 
  ggplot(aes(year, diff, group = lift, color = lift)) + 
  geom_line(show.legend = FALSE) + 
  geom_segment(aes(xend = 2019.1, yend = diff), linetype = 2, colour = 'grey', show.legend = FALSE) + 
  geom_point(size = 2, show.legend = FALSE) + 
  geom_text(aes(x = 2019.1, label = lift, color = "#000000"), hjust = 0, show.legend = FALSE) + 
  drop_axis(axis = "y") +
  transition_reveal(year) +
  coord_cartesian(clip = 'off') +
  theme(plot.title = element_text(size = 20)) +
  labs(title = 'Difference over time',
       y = 'Difference (kg)',
       x = element_blank()) + 
  theme(plot.margin = margin(5.5, 40, 5.5, 5.5))

b_gif <- animate(animation2, 
                 fps = 10, 
                 duration = 25,
        width = 800, height = 200, 
        renderer = gifski_renderer("./difference_over_time.gif"))

b_gif
```

***

- This graph resolves the issue where it keeps on appending the previous year's data

- The user is able to follow the change now and also able to visualize the trend

- But this graph gives a little bit different visual of the data

### Animation Composition

```{r viz-animation-03}
#install.packages("magick")
library(magick)
a_mgif <- image_read(a_gif)
b_mgif <- image_read(b_gif)

new_gif <- image_append(c(a_mgif[1], b_mgif[1]), stack = TRUE)
for(i in 2:250){
  combined <- image_append(c(a_mgif[i], b_mgif[i]), stack = TRUE)
  new_gif <- c(new_gif, combined)
}

new_gif
```

***
Placing Animations side-by-side with magick.

- Showing both the graphs together is a huge improvement as it lets the user see the actual values, the change and also the trend over years

- But there can be more ways to find out insights from the data and look at the distribution and hidden trends

- Also the visuals can be made interactive to engage the user more with the data, hence come our visuals and ideas


Our Visuals {.storyboard}
=========================================

### Visual 1

```{r Soumyadip Visual, fig.height=5, fig.width=10}
library(ggridges)
library(scales)
library(viridis)
library(forcats)
ipf_lifts %>%
  mutate(year = lubridate::year(date)) %>%
  filter(year == 1994) %>%
  mutate(age_class = fct_rev(as.factor(age_class))) %>%
  filter(age_class != '5-12') %>%
  filter(age_class != '80-999') %>%
  drop_na(c(age_class,best3squat_kg)) %>%
  ggplot(aes(x=best3squat_kg,y=age_class,fill=factor(..quantile..))) +
  stat_density_ridges(geom="density_ridges_gradient",calc_ecdf = TRUE,
                      quantiles = 4,quantile_lines = TRUE,
                      na.rm = TRUE) +
  scale_fill_viridis(discrete = TRUE,name="Quartiles") +
  scale_x_continuous(limits = c(0,510),expand = c(0,0),labels=unit_format(unit="Kg")) +
  theme_bw() +
  labs(
    title = "Distribution of Weight lifted in Squat for different Age Groups",
    x = "",
    y = "Age Classification"
  )
```

***

- This visual shows the distribution of weights lifted by different age groups with the quartiles separated by color (Viridis)

- The third quartile (Green) contains 50 to 75 precentile points and shows that people of ages between 24 to 34 lift the highest weights

- Beyond that age group, the capability of lifting weights decreases gradually

### Visual 2

```{r RamyaPrakash Visuals}
library(gganimate)
library(gifski)
library(ggridges)
library(scales)
library(viridis)

ipf_lifts_year <- ipf_lifts %>% 
                  mutate(year = format(date, "%Y")) %>%
                  mutate(decade = year(date) - (year(date) %% 10))

ipf_lifts_decade<- ggplot(data=ipf_lifts_year, mapping = aes(y=decade, x=best3deadlift_kg, fill=sex)) + 
geom_density_ridges() + 
labs(x = 	"Weight (kg)",y = "Decade",title = "How men and women differ in Best of 3 Deadlifts \n by decades") +
scale_fill_viridis(discrete = TRUE,name="Quartiles") + 
scale_x_continuous(limits = c(10,500))+
theme_ridges()+ 
transition_manual(year)

my_gif <- animate(ipf_lifts_decade, 
                 fps = 5, 
                 duration = 5,
        renderer = gifski_renderer("./ipf_lifts_decade.gif"))

my_gif
```

***

This animated vizualization shows how men and women differ in Best of 3 deadlifts by decades.


-Male lifters are able to lift higher maximal weights than women. 

-In the inital decade there is no data available for women participants.

-There is no trend shown in maximum weights lifted over the time in men or women. 

-In the Recent decades there is an increase in data available. 


### Visual 3

```{r Shruti Visuals}
ipf <- ipf_lifts %>% 
  mutate(year = as.numeric(format(date, '%Y'))) %>% 
  select(-event, -division, -federation, -date) %>% 
  gather(activity, weight, best3squat_kg:best3deadlift_kg) %>% 
  group_by(year)
max <- ipf %>%
  group_by(sex, activity, year) %>%
  filter(place != "DD" & place != "DQ" & !is.na(weight) & weight == max(weight, na.rm = T),
         year >= 1980) %>% 
  ungroup() %>% 
  group_by (name) %>% 
  mutate(activity = recode(activity, "best3bench_kg" = "Bench", 
  "best3deadlift_kg" = "Deadlift", "best3squat_kg" = "Squat"),
  sex = recode(sex, "F" = "Female", 'M' = "Male")) %>%
  arrange(desc(year))
competition <- max %>% 
  group_by(sex, name) %>% 
  summarise(n = n()) %>% 
  group_by(sex, n) %>% 
  summarise(total = n())
#number of max achieved
g_count <- ggplot(competition, aes(x = n, y= total, fill = sex)) +
  geom_bar(stat = "identity")+
  facet_grid(sex ~ .)+
  scale_x_continuous(breaks = seq(0,12,1))+
  scale_y_continuous(breaks = seq(0,50,5))+
  labs(y= "# of participants reaching the max", x = "Number of max achieved", 
       title = "How do the max achievements distribute across participants?",
       subtitle = "",
       caption = "")+
  theme(
    strip.text = element_blank(),
    plot.title = element_text(size = 12),
    legend.title = element_blank(),
    legend.position='top',
    legend.background = element_rect(fill = "transparent"),
    legend.box.background = element_rect(fill = "transparent"),
    legend.spacing.x = unit(0.4, 'cm'),
    legend.text = element_text(size = 12),
  )

g_count
```

***

- Comments of Visual 3

Conclusion {.storyboard}
=========================================

### THANK YOU!!

```{r conclusion-01,fig.height=5, fig.width=8}
library(jpeg)

pg <- readJPEG("img/ThankYou.jpg")
plot.new() 
rasterImage(pg,0,0,1,1)
```


***


Our GitHub repo: https://github.com/RamyaPrakashPT/DataVisualization-DesignContest

Our Rpubs link: http://rpubs.com/soumyadipmitra/designcontest


Our BlogPost links: